| 1. |
- Brueffer, Christian, et al.
(author)
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Biopython Project Update 2016
- 2016
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Conference paper (other academic/artistic)abstract
- The Biopython Project is a long-running distributed collaborative effort, supported by the Open Bioinformatics Foundation, which develops a freely available Python library for biological computation.We present here details of the latest Biopython release - version 1.66. New features include: extended Bio.KEGG and Bio.Graphics modules to support drawing KEGG pathways with transparency; extended “abi” Bio.SeqIO parser to decode almost all documented fields used by ABIF instruments; a QCPSuperimposer module using the Quaternion Characteristic Polynomial algorithm for superimposing structures to Bio.PDB; and an extended Bio.Entrez module to implement the NCBI Entrez Citation Matching function and to support NCBI XML files with XSD schemas. Additionally we fixed miscellaneous bugs, enhanced our test suite and continued our efforts to abide by the PEP8 coding style guidelines.We are currently preparing a new release – version 1.67 – that will deprecate the ability to compare SeqRecord objects with “==”, which sometimes lead to surprising results. In addition it will feature a new experimental Bio.phenotype module for working with Phenotype Microarray data; updates to Bio.Data toinclude NCBI genetic code table 25, covering Candidate Division SR1 and Gracilibacteria; an update to Bio.Restriction to include the REBASE May 2016 restriction enzyme list; updates to BioSQL to use foreign keys with SQLite3 databases; as well as corrections to the Bio.Entrez module and the MMCIF structure parser.Our website has been migrated from MediaWiki to GitHub Pages and is now under version control. The continuous integration process on GitHub has been enhanced by including external services like Landscape, Quantified Code and Codecov to perform quality review, test coverage analysis and generation of quality metrics.Finally, our range of Docker containers has been greatly enhanced. In addition to a basic container that includes Python 2 and 3 with Biopython and all its dependencies, as well as a BioSQL container, we now also provide two versions of Jupyter notebook containers: a basic one, and a version including the Biopython tutorial as notebooks.
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| 2. |
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| 3. |
- Grüning, Björn, et al.
(author)
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Bioconda: A sustainable and comprehensive software distribution for the life sciences
- 2017
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Other publication (other academic/artistic)abstract
- We present Bioconda (https://bioconda.github.io), a distribution of bioinformatics software for the lightweight, multi-platform and language-agnostic package manager Conda. Currently, Bioconda offers a collection of over 3000 software packages, which is continuously maintained, updated, and extended by a growing global community of more than 200 contributors. Bioconda improves analysis reproducibility by allowing users to define isolated environments with defined software versions, all of which are easily installed and managed without administrative privileges.
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| 4. |
- Hyde, Kevin D., et al.
(author)
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One stop shop: backbones trees for important phytopathogenic genera: I (2014)
- 2014
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In: Fungal diversity. - : Springer Science and Business Media LLC. - 1560-2745 .- 1878-9129. ; 67:1, s. 21-125
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Journal article (peer-reviewed)abstract
- Many fungi are pathogenic on plants and cause significant damage in agriculture and forestry. They are also part of the natural ecosystem and may play a role in regulating plant numbers/density. Morphological identification and analysis of plant pathogenic fungi, while important, is often hampered by the scarcity of discriminatory taxonomic characters and the endophytic or inconspicuous nature of these fungi. Molecular (DNA sequence) data for plant pathogenic fungi have emerged as key information for diagnostic and classification studies, although hampered in part by non-standard laboratory practices and analytical methods. To facilitate current and future research, this study provides phylogenetic synopses for 25 groups of plant pathogenic fungi in the Ascomycota, Basidiomycota, Mucormycotina (Fungi), and Oomycota, using recent molecular data, up-to-date names, and the latest taxonomic insights. Lineage-specific laboratory protocols together with advice on their application, as well as general observations, are also provided. We hope to maintain updated backbone trees of these fungal lineages over time and to publish them jointly as new data emerge. Researchers of plant pathogenic fungi not covered by the present study are invited to join this future effort. Bipolaris, Botryosphaeriaceae, Botryosphaeria, Botrytis, Choanephora, Colletotrichum, Curvularia, Diaporthe, Diplodia, Dothiorella, Fusarium, Gilbertella, Lasiodiplodia, Mucor, Neofusicoccum, Pestalotiopsis, Phyllosticta, Phytophthora, Puccinia, Pyrenophora, Pythium, Rhizopus, Stagonosporopsis, Ustilago and Verticillium are dealt with in this paper.
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| 5. |
- Moeller, Steffen, et al.
(author)
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Community-driven development for computational biology at Sprints, Hackathons and Codefests
- 2014
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In: BMC Bioinformatics. - 1471-2105. ; 15, s. S7-
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Journal article (peer-reviewed)abstract
- Background: Computational biology comprises a wide range of technologies and approaches. Multiple technologies can be combined to create more powerful workflows if the individuals contributing the data or providing tools for its interpretation can find mutual understanding and consensus. Much conversation and joint investigation are required in order to identify and implement the best approaches. Traditionally, scientific conferences feature talks presenting novel technologies or insights, followed up by informal discussions during coffee breaks. In multi-institution collaborations, in order to reach agreement on implementation details or to transfer deeper insights in a technology and practical skills, a representative of one group typically visits the other. However, this does not scale well when the number of technologies or research groups is large. Conferences have responded to this issue by introducing Birds-of-a-Feather (BoF) sessions, which offer an opportunity for individuals with common interests to intensify their interaction. However, parallel BoF sessions often make it hard for participants to join multiple BoFs and find common ground between the different technologies, and BoFs are generally too short to allow time for participants to program together. Results: This report summarises our experience with computational biology Codefests, Hackathons and Sprints, which are interactive developer meetings. They are structured to reduce the limitations of traditional scientific meetings described above by strengthening the interaction among peers and letting the participants determine the schedule and topics. These meetings are commonly run as loosely scheduled unconferences (self-organized identification of participants and topics for meetings) over at least two days, with early introductory talks to welcome and organize contributors, followed by intensive collaborative coding sessions. We summarise some prominent achievements of those meetings and describe differences in how these are organised, how their audience is addressed, and their outreach to their respective communities. Conclusions: Hackathons, Codefests and Sprints share a stimulating atmosphere that encourages participants to jointly brainstorm and tackle problems of shared interest in a self-driven proactive environment, as well as providing an opportunity for new participants to get involved in collaborative projects.
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| 6. |
- Phillips-Howard, Penelope A, et al.
(author)
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Deaths Ascribed to Non-Communicable Diseases among Rural Kenyan Adults Are Proportionately Increasing : Evidence from a Health and Demographic Surveillance System, 2003-2010
- 2014
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In: PLOS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 9:11
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Journal article (peer-reviewed)abstract
- BACKGROUND: Non-communicable diseases (NCDs) result in more deaths globally than other causes. Monitoring systems require strengthening to attribute the NCD burden and deaths in low and middle-income countries (LMICs). Data from health and demographic surveillance systems (HDSS) can contribute towards this goal.METHODS AND FINDINGS: Between 2003 and 2010, 15,228 deaths in adults aged 15 years (y) and older were identified retrospectively using the HDSS census and verbal autopsy in rural western Kenya, attributed into broad categories using InterVA-4 computer algorithms; 37% were ascribed to NCDs, 60% to communicable diseases (CDs), 3% to injuries, and <1% maternal causes. Median age at death for NCDs was 66y and 71y for females and males, respectively, with 43% (39% male, 48% female) of NCD deaths occurring prematurely among adults aged below 65y. NCD deaths were mainly attributed to cancers (35%) and cardio-vascular diseases (CVDs; 29%). The proportionate mortality from NCDs rose from 35% in 2003 to 45% in 2010 (χ2 linear trend 93.4; p<0.001). While overall annual mortality rates (MRs) for NCDs fell, cancer-specific MRs rose from 200 to 262 per 100,000 population, mainly due to increasing deaths in adults aged 65y and older, and to respiratory neoplasms in all age groups. The substantial fall in CD MRs resulted in similar MRs for CDs and NCDs among all adult females by 2010. NCD MRs for adults aged 15y to <65y fell from 409 to 183 per 100,000 among females and from 517 to 283 per 100,000 population among males. NCD MRs were higher among males than females aged both below, and at or above, 65y.CONCLUSIONS: NCDs constitute a significant proportion of deaths in rural western Kenya. Evidence of the increasing contribution of NCDs to overall mortality supports international recommendations to introduce or enhance prevention, screening, diagnosis and treatment programmes in LMICs.
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| 7. |
- Singh, Sourav, et al.
(author)
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Biopython Project Update 2017
- 2017
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Conference paper (other academic/artistic)abstract
- The Biopython Project is a long-running distributed collaborative effort, supported by the Open Bioinformatics Foundation, which develops a freely available Python library for biological computation [1]. We present here details of the Biopython releases since BOSC 2016, namely Biopython 1.68, 1.69 and 1.70. Together these had 82 named contributors including 51 newcomers which reflects our policy of trying to encourage even small contributions.Biopython 1.68 (August 2016) was a relatively small release, with the main new feature being support for RSSB’s new binary Macromolecular Transmission Format (MMTF) for structural data.Biopython 1.69 (April 2017) represents the start of our re-licensing plan, to transition away from our liberal but unique Biopython License Agreement to the similar but very widely used 3-Clause BSD License. We are reviewing the code base authorship file-by-file, in order to gradually dual license the entire project. Major new features include: a new parser for the ExPASy Cellosaurus cell line database, catalogue and ontology; support for the UCSC Multiple Alignment Format (MAF), FSA sequencing files, version 4 of the Affymetrix CEL format; updates to the REBASE February 2017 restriction enzyme list; Bio.PDB.PDBList now can download more formats including MMTF; enhanced PyPy support by taking advantage of NumPy and compiling most of the Biopython C code modules.Biopython 1.70 (July 2017) has internal changes to better support the now standard pip tool for Python package installation. Major new features include: support for Mauve’s eXtended Multi-FastA (XMFA) file format, updates to our BLAST XML and MEME parsers, ExPASy support, and phylogenetic distance matrices. This release is noteworthy for our new logo, contributed by Patrick Kunzmann. This draws on our original double helix logo, and the blue and yellow colors of the current Python logo.All releases fixed miscellaneous bugs, enhanced the test suite, and continued efforts to follow the PEP8 and PEP257 coding style guidelines which is now checked automatically with GitHub-integrated continuous integration testing using TravisCI. We now also use AppVeyor for continuous integration testing under Windows. Current efforts include improving the unit test coverage, which is easily viewed online at CodeCov.io.
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